Across the United States, communities are being asked to make room for a new kind of industrial giant.
It may not have smokestacks, assembly lines or crowds of shift workers. From the outside, it can look like a windowless warehouse surrounded by fencing, power equipment and cooling systems. But inside, thousands of computer servers may be running the artificial intelligence tools, streaming services, business software and cloud platforms that now shape everyday life.
These buildings are data centers, and they are expanding rapidly.
The immediate reason is artificial intelligence. Training and operating advanced A.I. systems requires extraordinary amounts of computing power. That computing power, in turn, requires electricity, cooling, land, transmission lines and backup systems. As technology companies race to build larger A.I. systems, utilities and local governments are confronting a question that once seemed remote: How much public infrastructure should be devoted to the computing industry, and under what conditions?
The debate is sometimes presented as a choice between technological progress and environmental protection. The evidence suggests a more complicated problem.
Data centers provide services that businesses, hospitals, schools, governments and consumers increasingly rely on. They can also bring construction spending, tax revenue and investment in local infrastructure. But they can place substantial demands on electric grids and water systems, while creating fewer permanent jobs than their size and cost might suggest.
The central policy question, then, is not whether data centers should exist. It is how to decide where they belong, how quickly they should be built and who should pay for the systems required to support them.
A New Kind of Power Demand
Electric grids are designed around a basic constraint: supply and demand must remain in balance almost every moment of the day.
A large data center can complicate that balance. Some proposed campuses may require hundreds of megawatts of electricity, placing them in the same broad category as major industrial facilities or, in some cases, small cities.
That demand does not arrive by itself. It may require new substations, transmission lines, power plants or energy-storage systems. It can also create risks if developers reserve large amounts of grid capacity for projects that are delayed, scaled back or never built.
The uncertainty is especially pronounced because future demand for A.I. computing is difficult to predict. The industry may continue growing rapidly. But chips may also become more efficient, software may require less computing power, and some projects may prove uneconomic.
That leaves utilities with a difficult planning problem. If they build too little infrastructure, new projects may be delayed and electric reliability may suffer. If they build too much, households and ordinary businesses could be left paying for equipment that was designed for customers who never arrived.
One of the clearest principles to emerge from the evidence is that large new customers should generally bear the costs they directly cause.
In practice, that could mean special electric rates, connection charges, long-term contracts or financial guarantees. A data center that requires a new substation or transmission upgrade would help pay for it. If the project is canceled, other customers would be protected from absorbing the remaining cost.
The principle is simple. The implementation is not.
Electric grids are shared systems. A new transmission line may serve both a data center and the broader region. A power plant built partly in response to A.I. demand may also improve reliability for nearby communities. Regulators must therefore decide which expenses are attributable to a particular project and which provide wider public benefits.
There is no perfect formula. But failing to ask the question can amount to an invisible subsidy.
The Water Question
Electricity is only part of the story.
Servers generate heat, and that heat must be removed. Some data centers rely heavily on evaporative cooling, which can reduce electricity use but consume significant amounts of water. Others use air-based or “dry” cooling, which generally saves water but may require more power, particularly during hot weather.
That tradeoff makes national averages less useful than local conditions.
A water-intensive cooling system may be manageable in a region with abundant water and a mild climate. The same system may be difficult to justify in a drought-prone area where households, farms and ecosystems are already competing for limited supplies.
The most defensible approach is therefore not a universal ban on water cooling, but a rule tied to local scarcity.
In water-stressed regions, data centers can be required to use recycled water, limit potable-water consumption, reduce withdrawals during droughts or rely more heavily on dry and hybrid cooling systems. In places with more secure supplies, regulators may reasonably allow greater water use if it reduces electricity demand and does not threaten other users.
Here, too, disclosure matters. Annual water totals can obscure the moments when systems are under the greatest pressure. A facility that appears modest on a yearly basis may consume much more during the hottest and driest months.
Seasonal reporting would make those risks easier to see.
Promises of Clean Energy
Technology companies often say that their facilities are powered by renewable energy. That claim can be accurate and still incomplete.
A company may buy enough renewable electricity over the course of a year to match its annual consumption. But the wind does not always blow, the sun does not always shine, and the data center may continue operating around the clock.
As a result, a facility that is “100 percent renewable” on an annual accounting basis may still depend on fossil-fuel generation during particular hours.
A stronger standard would consider not only how much clean energy a company buys, but when and where that energy is available. It would also ask whether the company’s purchase helped create new power generation or simply claimed credit for a resource that already existed.
This idea is known as additionality. In plain terms, it asks whether the project caused more energy supply to be built.
A serious energy plan for a large data center may include several resources: renewable generation, batteries, transmission upgrades, nuclear power, geothermal energy or other dependable sources. The right combination varies by region.
What matters is that the new demand be matched by credible additions to the power system, rather than by paperwork alone.
Can Data Centers Help the Grid?
Data centers are often described as inflexible users of electricity. That is only partly true.
Some computing tasks must happen immediately. A search request, financial transaction or medical application cannot simply be delayed for several hours.
Other tasks may be more flexible. Training an A.I. model, processing a large batch of data or running certain maintenance operations may be shifted away from periods when the grid is under stress.
That flexibility could become valuable.
A data center might agree to reduce electricity use during emergencies, move nonurgent computing to another hour, rely temporarily on batteries or accept interruptible service for part of its demand. In exchange, it might receive a faster grid connection or a lower rate.
The promise is real, but the evidence is still developing. It is not enough for a company to say that its workload is flexible. The commitment must be measurable, enforceable and tested under real conditions.
Otherwise, flexibility becomes a talking point rather than a grid resource.
The Jobs Question
Local officials often support data centers because they promise investment and tax revenue.
During construction, the projects can create substantial demand for electricians, engineers, equipment operators and other skilled workers. Once completed, however, data centers are highly automated and may employ relatively modest numbers of permanent workers compared with factories, hospitals or large office campuses.
That does not mean they provide no economic benefit. A facility can expand the tax base, support local contractors and increase demand for certain technical services.
But it does mean that public subsidies deserve careful scrutiny.
States and localities sometimes offer sales-tax exemptions, property-tax abatements or infrastructure support to attract these projects. The relevant question is not whether a company will invest billions of dollars. It is whether the public benefits exceed the value of the incentives and the costs imposed on local systems.
A sound analysis would include permanent employment, tax revenue, grid and water expenses, environmental effects and the opportunity cost of using scarce infrastructure for one project rather than another.
It would also ask a question that is often overlooked: Would the company have built there anyway?
A region with inexpensive electricity, available land, strong fiber connections and a favorable permitting process may already be attractive. In that case, a large subsidy may reward a decision that did not need to be purchased.
Where Data Centers Make the Most Sense
The evidence points toward a policy of conditional development.
That means directing projects toward locations where the overall burden is lowest: areas with available grid capacity, access to dependable energy, manageable water conditions, industrial zoning and some distance from homes.
Communities can also publish maps showing where large electric loads can be connected most easily. Such maps would reduce speculation and help developers focus on places where infrastructure already exists or can be expanded at reasonable cost.
Waste heat offers another possibility. In some settings, heat from servers can be used in nearby buildings or industrial processes. But the opportunity is highly dependent on location and economics. It should be considered where practical, not treated as a universal solution.
The larger point is that a megawatt of data-center demand does not have the same consequences everywhere.
A project placed near existing power and transmission may be relatively easy to accommodate. The same project in a constrained region may require years of construction and billions of dollars in upgrades.
Good siting policy recognizes that difference.
What an Evidence-Based Policy Would Look Like
A workable system would begin before construction.
Developers would provide realistic forecasts of electricity and water demand, including how quickly the facility would reach full capacity. Utilities and local governments would review those forecasts independently rather than relying solely on company estimates.
Projects would be required to pay for infrastructure directly linked to their needs and to provide financial protection in case plans change.
Water rules would reflect local conditions. Energy claims would be based on new and dependable supply, not annual accounting alone. Promises to reduce electricity use during emergencies would be written into contracts and tested.
Performance would be measured over time.
That last step matters because the technology is changing quickly. A cooling system that appears efficient today may be outdated within a decade. A fixed rule requiring a particular machine or design could become obsolete. Standards based on outcomes, including energy use, water consumption, emissions and grid performance, are more likely to remain useful.
Phased approvals may also be appropriate. Instead of authorizing an enormous campus all at once, regulators could allow expansion in stages as power supply, transmission and water capacity become available.
That would reduce the risk of overbuilding while preserving the option to grow.
Neither a Ban Nor a Blank Check
The public debate often gravitates toward simple positions.
One side warns that data centers will overwhelm electric grids and drain water supplies. The other argues that any restriction risks surrendering leadership in artificial intelligence.
Neither position fully reflects the evidence.
The risks are real, but they are not uniform. Some projects may be well suited to their locations and capable of paying their own way. Others may impose costs that exceed their local benefits.
The most defensible approach is neither prohibition nor automatic approval. It is to treat data centers as what they have become: major industrial users of public systems.
That means asking the same questions society asks of other large developments. How much power and water will the project require? What infrastructure must be built? Who will pay? What happens if demand falls short? What benefits will remain after construction ends?
The answers will differ from place to place.
But the underlying principle is consistent: Communities should not have to choose between technological progress and responsible planning. They should require the two to proceed together.
Evidence & Source Transparency
Evidence First shows its work. The article ends above; this section is included so readers can inspect the main sources behind the factual claims.
The list below does not source every sentence. It focuses on the factual claims most important to the argument.
1. Data center electricity demand
Claim or topic:
Data centers already consume substantial amounts of electricity, and artificial intelligence is expected to accelerate that demand.
Source:
Lawrence Berkeley National Laboratory, 2024 United States Data Center Energy Usage Report
Source type:
Government-sponsored research and estimate.
What it supports:
The report estimates that U.S. data centers consumed about 176 terawatt-hours of electricity in 2023, equal to roughly 4.4 percent of national electricity use. It projects significant further growth through 2028 under several scenarios.
Important caveat:
Future demand is uncertain. The projections depend on assumptions about A.I. adoption, equipment efficiency, facility construction and computing workloads.
2. Global growth and the role of A.I.
Claim or topic:
A.I. is helping drive rapid growth in data center electricity demand, but it is not the only workload performed in data centers.
Source:
International Energy Agency, Energy and AI
Source type:
Expert organization analysis and modeling.
What it supports:
The IEA estimates that global data center electricity consumption could more than double by 2030, reaching about 945 terawatt-hours in its base-case projection. It also explains that A.I. is one contributor alongside cloud services, data storage and other digital activity.
Important caveat:
This is a modeled projection, not a measured future outcome. Actual demand may be higher or lower depending on technology, economics and deployment.
3. Energy supply and clean-energy claims
Claim or topic:
Meeting new data center demand will require additional electricity supply, and annual renewable-energy matching does not by itself show what powers a facility during every hour.
Source:
International Energy Agency, Energy Supply for AI
Source type:
Expert organization analysis and estimate.
What it supports:
The IEA projects that renewables will meet nearly half of the additional global data center demand through 2030, while natural gas, coal and, increasingly, nuclear power will also contribute. This supports the article’s conclusion that data center expansion is not automatically clean or fossil-fuel-dependent. The result depends on which resources are built and available.
Important caveat:
The source models the overall electricity mix. The article’s discussion of hourly matching and additionality is a policy interpretation that requires project-specific information to apply.
4. Cooling and water use
Claim or topic:
Data center cooling involves tradeoffs between electricity and water use, so the most appropriate system depends partly on climate and local water conditions.
Source:
U.S. Department of Energy, Best Practices Guide for Energy-Efficient Data Center Design
Source type:
Government technical guidance.
What it supports:
The guide describes cooling-system design, water-use efficiency and methods for reducing the energy and water required to remove heat from computing equipment. It supports evaluating cooling choices by performance and local conditions rather than prescribing one technology everywhere.
Important caveat:
The guide provides technical best practices. It does not establish a universal legal standard or determine which water restrictions a particular community should adopt.
5. Reclaimed water as a practical option
Claim or topic:
Recycled water can reduce a data center’s dependence on potable groundwater where suitable infrastructure is available.
Source:
U.S. Environmental Protection Agency, Water Reuse Case Study: Quincy, Washington
Source type:
Government case study.
What it supports:
The case study describes a partnership between Microsoft and the City of Quincy that treats and recirculates data center cooling water, reducing reliance on local potable groundwater. It provides a real-world example of the water-reuse approach discussed in the article.
Important caveat:
This is one local case study, not proof that reclaimed-water systems will be technically or economically practical at every data center.
6. Flexible electricity demand
Claim or topic:
Some data center computing may be shifted or reduced during periods of grid stress, but the practical scale of that flexibility is not yet well established.
Source:
Lawrence Berkeley National Laboratory, DOE Data Center Load Flexibility Workshop Summary
Source type:
Government-sponsored expert workshop and analysis.
What it supports:
Participants identified options such as shifting workloads, using on-site energy resources and coordinating operations with utilities. They also identified technical, financial and regulatory barriers to large-scale implementation.
Important caveat:
This was a workshop summary, not a controlled study of operating data centers. It establishes technical possibilities and unresolved barriers, not guaranteed grid savings.
7. Grid planning and who pays
Claim or topic:
Large data centers can require major grid investments, creating difficult questions about which customers should pay for transmission and other infrastructure.
Source:
Federal Energy Regulatory Commission, Transmission Planning and Cost Allocation Explainer
Source type:
Federal regulatory guidance.
What it supports:
FERC explains that transmission planning must identify benefits and use transparent methods to allocate infrastructure costs. Related proceedings show that regulators are actively debating how costs associated with rapid data center growth should be divided among developers, utilities and customers.
Important caveat:
The principle that costs should follow benefits or causation does not produce a simple answer in every case. A transmission project may serve both a data center and the wider region.
8. Permanent jobs, tax revenue and subsidies
Claim or topic:
Data centers can generate substantial construction activity and local tax revenue, but their ongoing operations employ relatively few workers compared with their physical size and capital investment. The public value of tax incentives therefore depends on the specific project and location.
Source:
Virginia Joint Legislative Audit and Review Commission, Data Centers in Virginia and Data Center and Manufacturing Incentives Evaluation
Source type:
State legislative research and economic analysis.
What it supports:
The 2024 review found that most of the industry’s economic benefits in Virginia came from construction rather than ongoing operations. Industry representatives estimated that a typical 250,000-square-foot facility has about 50 full-time workers, roughly half of them contractors, compared with about 1,500 workers on site at the height of construction. The review also found that data centers can produce substantial local tax revenue.
The earlier incentives evaluation concluded that Virginia’s data center tax exemption influenced location and expansion decisions and produced moderate economic benefits. It also found that available information was insufficient to estimate the exemption’s full fiscal and economic effects accurately, supporting a case-by-case approach rather than a universal conclusion about subsidies.
Important caveat:
These findings are specific to Virginia, the country’s largest established data center market. Employment, tax revenue, infrastructure costs and the effectiveness of incentives may differ substantially in other states and communities.
How to read this evidence
This article is the author’s analysis. The sources above are provided so readers can see where the factual claims come from and judge the evidence for themselves. Some sources support direct facts, while others provide context, estimates, or background evidence.
Corrections and updates
If a factual error is identified, this post will be corrected in the web version with a dated note explaining the change. Because email versions cannot be edited after sending, the web version should be treated as the current version.